activity
20152024
most citedPre-trained Models for Natural Language Processing: A Survey

1.5k citations · 5.2k across the 127 of their papers we have counts for

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Showing 2017Show all

6 papers · 1 filter

cs.CL2017★ 8 cited

DAG-based Long Short-Term Memory for Neural Word Segmentation

Xinchi Chen, Zhan Shi, Xipeng Qiu +1

Neural word segmentation has attracted more and more research interests for its ability to alleviate the effort of feature engineering and utilize the external resource by the pre-…

cs.CL2017★ 1 cited

Overview of the NLPCC 2017 Shared Task: Chinese News Headline Categorization

Xipeng Qiu, Jingjing Gong, Xuanjing Huang

In this paper, we give an overview for the shared task at the CCF Conference on Natural Language Processing \& Chinese Computing (NLPCC 2017): Chinese News Headline Categorization.…

cs.CL2017★ 9 cited

Dynamic Compositional Neural Networks over Tree Structure

Pengfei Liu, Xipeng Qiu, Xuanjing Huang

Tree-structured neural networks have proven to be effective in learning semantic representations by exploiting syntactic information. In spite of their success, most existing model…

cs.CL2017★ 41 cited

Reinforced Mnemonic Reader for Machine Reading Comprehension

Minghao Hu, Yuxing Peng, Zhen Huang +3

In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects. First, a reattention m…

cs.CL2017★ 32 cited

Adversarial Multi-Criteria Learning for Chinese Word Segmentation

Xinchi Chen, Zhan Shi, Xipeng Qiu +1

Different linguistic perspectives causes many diverse segmentation criteria for Chinese word segmentation (CWS). Most existing methods focus on improve the performance for each sin…

cs.CL2017★ 91 cited

Adversarial Multi-task Learning for Text Classification

Pengfei Liu, Xipeng Qiu, Xuanjing Huang

Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features.…